Xiaowen Zhao
About Xiaowen Zhao
Xiaowen Zhao is a Senior Scientist at Bristol Myers Squibb in New Brunswick, New Jersey, with a background in chemical engineering and extensive experience in process development and analytical technology.
Title
Xiaowen Zhao currently holds the position of Senior Scientist at Bristol Myers Squibb in New Brunswick, New Jersey, United States.
Professional Experience at Corteva Agriscience
From 2019 to 2021, Xiaowen Zhao worked as a Process Development Engineer at Corteva Agriscience in the Indianapolis, Indiana Area. During this period, Zhao focused on the development and optimization of chemical processes, contributing to enhanced efficiency and scalability in manufacturing operations.
Academic Roles at University of Michigan
Xiaowen Zhao has held multiple academic positions at the University of Michigan, including positions as a Teaching Assistant in 2018 and a Graduate Teaching Assistant in 2016, each for a duration of 3 months. Additionally, Zhao served as a Research Assistant from 2014 to 2019, accumulating valuable research experience in the Greater Detroit Area.
Education and PhD in Chemical Engineering
Xiaowen Zhao earned a Doctor of Philosophy (PhD) degree in Chemical Engineering from the University of Michigan, studying from 2014 to 2018. This advanced education provided a strong foundation in chemical process design and development, facilitating career growth in scientific and engineering roles.
Undergraduate Studies at Shanghai Jiao Tong University
Xiaowen Zhao completed a Bachelor's degree in Chemical Engineering from Shanghai Jiao Tong University, studying from 2010 to 2014. During this period, Zhao also gained hands-on research experience as an Undergraduate Researcher from 2011 to 2012 and again in 2014.
Experience with Mettler Toledo AutoChem Technology
Xiaowen Zhao has extensive hands-on experience with Mettler Toledo AutoChem technology, including Optimax, RX-10, EasySampler, EasyViewer, and FBRM. This expertise supports Zhao's advocacy for Process Analytical Technology (PAT) tools to enable data-rich experimentation and drive the efficient design and development of chemical processes.